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Efficient processing and analysis of large-scale light-sheet microscopy data

Nature Protocols volume 10, pages 1679–1696 (2015)Cite this article

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Abstract

Light-sheet microscopy is a powerful method for imaging the development and function of complex biological systems at high spatiotemporal resolution and over long time scales. Such experiments typically generate terabytes of multidimensional image data, and thus they demand efficient computational solutions for data management, processing and analysis. We present protocols and software to tackle these steps, focusing on the imaging-based study of animal development. Our protocols facilitate (i) high-speed lossless data compression and content-based multiview image fusion optimized for multicore CPU architectures, reducing image data size 30–500-fold; (ii) automated large-scale cell tracking and segmentation; and (iii) visualization, editing and annotation of multiterabyte image data and cell-lineage reconstructions with tens of millions of data points. These software modules are open source. They provide high data throughput using a single computer workstation and are readily applicable to a wide spectrum of biological model systems.

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Figure 1: Overview of image processing and data analysis workflow.
Figure 2: Lossless image compression and content-based multiview fusion.
Figure 3: Performance comparison of lossless image compression formats.
Figure 4: Multiview image data compaction for light-sheet microscopy.
Figure 5: Image compression performance using multicore CPUs.
Figure 6: Image annotation and editing of cell-lineage data using CATMAID.
Figure 7: Application example in Drosophila development.

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Acknowledgements

We thank A. Cardona and the participants of the Janelia CATMAID hackathon for help with modifying the open-source code of CATMAID; K. Khairy for his contributions to exploring approaches to multiview image fusion and SiMView data management; and K. Branson and A. Cardona for helpful comments on the manuscript. This work was supported by the Howard Hughes Medical Institute.

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Authors and Affiliations

  1. Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA

    Fernando Amat, Burkhard Höckendorf, Yinan Wan, William C Lemon, Katie McDole & Philipp J Keller

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  1. Fernando Amat
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  2. Burkhard Höckendorf
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  4. William C Lemon
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  6. Philipp J Keller
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Contributions

F.A. and B.H. developed the KLB file format and related software infrastructure. P.J.K. developed the multiview registration and fusion software, with contributions from F.A. F.A. developed the TGMM framework and related software infrastructure. Y.W., W.C.L. and K.M. performed light-sheet microscopy experiments and contributed image data sets. F.A. and P.J.K. wrote the manuscript, with input from all authors.

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Correspondence to Fernando Amat or Philipp J Keller.

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Integrated supplementary information

Supplementary Figure 1 Local performance of lossless compression image file formats

Performance of the KLB lossless compression format vs. LZW-TIFF (green) and JPEG 2000 (blue) lossless compression formats with respect to write speed (first column) and read speed (second column). The JPEG 2000 benchmark utilizes the multi-threaded commercial library PICTools Medical SDK (Accusoft). A performance comparison of KLB and uncompressed TIFF formats is included as well (orange). LZW-TIFF and uncompressed TIFF benchmarks utilize the imread and imwrite functions provided by the Image Processing Toolbox in Matlab. All performance data are provided as ratios with KLB performance in the numerator, i.e. ratios larger than one (grey lines) indicate superior performance of the KLB format. The comparison was performed using a variety of fluorescence microscopy image data sets stored locally on a high-performance RAID array built from solid-state drives (SSDs) and thus complements the network-based analysis shown in Fig. 3 (note that performance with respect to compression ratios is identical to the data shown in Fig. 3). Benchmark data sets include SiMView light-sheet microscopy recordings of fruit fly, mouse and zebrafish embryonic development (data sets 1-8), confocal microscopy data of a zebrafish embryo (data set 9) and SiMView functional image data of brain activity in a larval zebrafish (data set 10). Developmental data sets (data sets 1-8) were analyzed as raw and masked versions in order to illustrate the importance of background masking for maximizing data storage and access efficiency. Please see Steps I-III in Fig. 2 for a description of the concepts underlying background masking.

Supplementary Figure 2 Block-size dependency of KLB file size and read/write speeds

Performance comparison for KLB versus JPEG 2000 (JP2) with respect to file size (a), write time (b) and read time (c), as a function of KLB block size (in pixels). The results represent average performance across five data sets, including developmental image data from a fruit fly embryo, a zebrafish embryo and early-/late-stage mouse embryos as well as functional image data from a zebrafish larva. The larger the block size, the better the KLB compression ratio; however, this ratio reaches saturation already for relatively small block sizes. Read and write times are not optimal for extreme block sizes, i.e. both for very small and for very large blocks. If blocks are too small, communication overhead in processing threads becomes an issue. If blocks are too large, computations cannot be parallelized to the maximum extent (in the most extreme scenario, a single thread has to handle the entire image). The figure shows a diagonal band, where all three metrics are optimal or near optimal at the same time. Based on these benchmarks, we chose the default block size as 96 x 96 x 8 pixels. The JPEG 2000 benchmark utilizes the multi-threaded commercial library PICTools Medical SDK (Accusoft). Lateral size refers to the X and Y axes of the image volume. Axial size refers to the Z axis of the image volume, which is typically smaller than the lateral size in light microscopy due to anisotropic spatial resolution in the microscope and anisotropic spatial sampling of the specimen volume.

Supplementary Figure 3 KLB performance comparison for local vs. network data storage

Comparison of KLB read and write speeds on a local data drive versus a data drive mounted over the network (using a 10 Gb/s glass fiber connection). Speeds are comparable since most of the time is spent on data compression and decompression, respectively, and physical disk access introduces relatively little overhead. Moreover, most modern operating systems and RAID hardware improve I/O performance by caching and by using dedicated processors that avoid load on primary CPUs. Thus, while some blocks are compressed or decompressed others are written or read, respectively, masking I/O costs. All data points are averages based on n = 5 iterations of the benchmark.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Note 1, Supplementary Table 1 (PDF 1261 kb)

Supplementary Software 1

KLB lossless compression file format. This software package contains the C++11 source code for the KLB file format implementation as well as wrappers for Matlab and Java. The folder bin contains the precompiled static and shared (DLL) libraries for Windows 7 64-bit as well as a simple executable test_KLBIO.exe for testing read/write operations. The source code of this executable represents a good example of how to use the API for the KLB file format. For Windows 7 64-bit, we also provide precompiled MEX files in the folder matlabWrapper. Linux and Mac OS users need to compile both the source code and the Matlab wrappers to obtain libraries and executables. For the first part, a CMake file is available in the folder src. For the second part, the folder matlabWrapper contains the script compileMex.m for generating MEX files. The C++ libraries need to be compiled in release mode before compiling the MEX files. In order to keep track of possible software updates, the user can also clone all files from the primary public software repository using the following git command: git clone https://fernandoamat@bitbucket.org/fernandoamat/keller-lab-block-filetype.git (ZIP 4460 kb)

Supplementary Software 2

KLB Java Native Interface library and SCIFIO implementation. This software package exposes the C++ API on the Java side and includes a functional implementation of a SCIFIO format that provides KLB support to image processing frameworks such as ImageJ and Knime. Precompiled native libraries for Windows and Linux (64-bit) are bundled inside the JAR file included in this software package. For convenience, ImageJ users can follow the update site at http://sites.imagej.net/SiMView (for instructions, see http://wiki.imagej.net/How_to_follow_a_3rd_party_update_site). (ZIP 1099 kb)

Supplementary Software 3

Image processing pipeline for light-sheet microscopy. This software package contains our Matlab code for image processing of light-sheet microscopy data sets, including (1) sCMOS image correction, background masking and KLB lossless image compression (using script clusterPT.m), (2) content-based multi-view image registration and fusion (using scripts clusterMF.m, localAP.m and clusterTF.m), (3) spatial drift correction and intensity normalization (using scripts localEC.m and clusterCS.m) and (4) adaptive local background correction (using script clusterFR.m). Please see the README file for detailed information about these software modules. (ZIP 1003 kb)

Supplementary Software 4

TGMM software for segmentation and cell tracking. This software package contains the C++ and CUDA source code for the Tracking with Gaussian Mixture Models (TGMM) software for automated segmentation and cell tracking in light microscopy time-lapse data sets. The software package includes the following folders: src: contains all source code files. This folder also includes the file CMakeList.txt that can be used to compile the source code. doc: contains the documentation of the TGMM software. bin: contains Windows 7 64bit executables for running the TGMM software. When compiling the source code, the executables for the release version will be placed here. This folder also contains all necessary DLLs (CUDA and MSVC runtime) as well as the text files containing machine learning classifiers for cell division detection. Please see the README file for detailed information on how to run and compile the TGMM software. In order to keep track of possible software updates, the user can also clone all files from the primary public software repository using the following git command: git clone git://git.code.sf.net/p/tgmm/code tgmm-code (ZIP 72857 kb)

Supplementary Software 5

CATMAID branch for 5D image visualization and lineage editing. This software package contains our branch of the open source software CATMAID. The software can also be cloned using the following git command: git clone -b 5Dvisualization --single-branch https://fernandoamat@bitbucket.org/fernandoamat/catmaid_5d_visualization_annotation.git The PDF file UserGuide.pdf in the root folder of this software package and the website http://catmaid.org/ provide detailed instructions for setting up a CATMAID server. (ZIP 19979 kb)

Supplementary Software 6

Matlab import/export scripts for TGMM, CATMAID and Imaris. This software package contains Matlab code for transferring results between TGMM, CATMAID and Imaris. In order to optimize read speed, the code for reading XML files generated by TGMM needs to be compiled into MEX files. The folder readTGMM_XMLoutput contains the script compileMex.m for this purpose. The README file contains further details on this topic and a description of the main Matlab functions included in this software package. Briefly, these Matlab functions facilitate: (1) import of TGMM tracking and segmentation results into Matlab, (2) export of image data and tracking results from Matlab to CATMAID, (3) import of cell lineage information from CATMAID into Matlab, (4) export of cell lineage information from Matlab to Imaris. (ZIP 3470 kb)

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Amat, F., Höckendorf, B., Wan, Y. et al. Efficient processing and analysis of large-scale light-sheet microscopy data. Nat Protoc 10, 1679–1696 (2015). https://doi.org/10.1038/nprot.2015.111

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